ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

Ma, Yuhui; Hao, Honggang; Xie, Jianyang; Fu, Huazhu; Zhang, Jiong; Yang, Jianlong; Wang, Zhen; Liu, Jiang; Zheng, Yalin; Zhao, Yitian

doi:10.1109/tmi.2020.3042802

Cited by 205 publications

(168 citation statements)

References 59 publications

(94 reference statements)

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“…Box counting (D box values) from Fraclab ( https://www.project.inria.fr/fraclab/ ) was applied to quantify the complexity in all three capillary plexuses (i.e., SCP, DCP, and WCP) in the 2.5-mm diameter of the fovea. The methods above were implemented using MATLAB 2016b (Mathworks, Inc., Natick, MA, USA) ( 24 ).…”

Section: Methodsmentioning

confidence: 99%

Superficial Macula Capillary Complexity Changes Are Associated With Disability in Neuromyelitis Optica Spectrum Disorders

Wei

Xie

et al. 2021

Front. Neurol.

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Purpose: We examined the macular microvascular changes of the macula in neuromyelitis optica spectrum disorder (NMOSD) patients and its association with their disability and other clinical variables.Methods: Thirty-four NMOSD (13 patients without optic neuritis, NMOSD-NON, and 21 patients with a history of optic neuritis, NMOSD-ON) and 44 healthy controls (HCs) were included in the study. Optical coherence tomographic angiography (OCTA) was used to image the superficial (SCP), deep (DCP), and whole capillary plexus (WCP) in a 2.5-mm-diameter concentric circle [excluding the foveal avascular zone (FAZ)]. An algorithm (Dbox) was used to quantify the complexity of the three capillary layers by fractal analysis. We also evaluated the expanded disability scale status (EDSS).Results: Dbox values were significantly reduced in SCP (p < 0.001), DCP (p < 0.001), and WCP (p = 0.003) of NMOSD when compared with HCs. Dbox values were significantly reduced in NMOSD eyes with optic neuritis when compared with healthy controls (p < 0.001) and eyes without optic neuritis (p = 0.004) in the SCP. In the DCP, eyes with optic neuritis showed significantly reduced Dbox values when compared with eyes without optic neuritis (p = 0.016) and healthy controls (p < 0.001); eyes without optic neuritis showed significantly reduced Dbox values (p = 0.007) in the DCP when compared with healthy controls. A significant negative correlation (Rho = −0.475, p = 0.005) was shown between the superficial macula Dbox values and the EDSS in NMOSD patients. Additionally, a negative correlation (Rho = −0.715, p = 0.006) was seen in the superficial Dbox values in [e]eyes without optic neuritis and EDSS.Conclusions: Macular microvascular damage in the superficial plexus is associated with disability in NMOSD. Macular microvascular alterations arise independently of the occurrence of ON in NMOSD.

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Section: Methodsmentioning

confidence: 99%

Superficial Macula Capillary Complexity Changes Are Associated With Disability in Neuromyelitis Optica Spectrum Disorders

Wei

Xie

et al. 2021

Front. Neurol.

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“…In humans, segmentation of fine capillary networks has been achieved in OCTA enface images (100) while in mice segmentation of retinal vasculature using OCTA has been reported for longitudinal monitoring of angiogenesis (101). Current advances applying deep learning to vessel segmentation continue to improve the performance of these methods and this has been helped by the public access to data sets (102).…”

Section: Vasculaturementioning

confidence: 99%

Quantitative Assessment of Experimental Ocular Inflammatory Disease

et al. 2021

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Ocular inflammation imposes a high medical burden on patients and substantial costs on the health-care systems that mange these often chronic and debilitating diseases. Many clinical phenotypes are recognized and classifying the severity of inflammation in an eye with uveitis is an ongoing challenge. With the widespread application of optical coherence tomography in the clinic has come the impetus for more robust methods to compare disease between different patients and different treatment centers. Models can recapitulate many of the features seen in the clinic, but until recently the quality of imaging available has lagged that applied in humans. In the model experimental autoimmune uveitis (EAU), we highlight three linked clinical states that produce retinal vulnerability to inflammation, all different from healthy tissue, but distinct from each other. Deploying longitudinal, multimodal imaging approaches can be coupled to analysis in the tissue of changes in architecture, cell content and function. This can enrich our understanding of pathology, increase the sensitivity with which the impacts of therapeutic interventions are assessed and address questions of tissue regeneration and repair. Modern image processing, including the application of artificial intelligence, in the context of such models of disease can lay a foundation for new approaches to monitoring tissue health.

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“…[15][16][17] In particular, AI techniques developed from artificial neural networks, deep learning (DL)-based algorithms show great promise in extracting features and learning patterns from complex data. 18 Indeed, in eyecare, DL techniques have helped physicians in detection of anatomical structures and/or lesions, [19][20][21][22] diagnosis of various diseases 23 and provided prognostic insights. 24 25 Most studies are, however, primarily focused on…”

Section: Introductionmentioning

confidence: 99%

Keratoconus detection of changes using deep learning of colour-coded maps

et al. 2021

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ObjectiveTo evaluate the accuracy of convolutional neural networks technique (CNN) in detecting keratoconus using colour-coded corneal maps obtained by a Scheimpflug camera.DesignMulticentre retrospective study.Methods and analysisWe included the images of keratoconic and healthy volunteers’ eyes provided by three centres: Royal Liverpool University Hospital (Liverpool, UK), Sedaghat Eye Clinic (Mashhad, Iran) and The New Zealand National Eye Center (New Zealand). Corneal tomography scans were used to train and test CNN models, which included healthy controls. Keratoconic scans were classified according to the Amsler-Krumeich classification. Keratoconic scans from Iran were used as an independent testing set. Four maps were considered for each scan: axial map, anterior and posterior elevation map, and pachymetry map.ResultsA CNN model detected keratoconus versus health eyes with an accuracy of 0.9785 on the testing set, considering all four maps concatenated. Considering each map independently, the accuracy was 0.9283 for axial map, 0.9642 for thickness map, 0.9642 for the front elevation map and 0.9749 for the back elevation map. The accuracy of models in recognising between healthy controls and stage 1 was 0.90, between stages 1 and 2 was 0.9032, and between stages 2 and 3 was 0.8537 using the concatenated map.ConclusionCNN provides excellent detection performance for keratoconus and accurately grades different severities of disease using the colour-coded maps obtained by the Scheimpflug camera. CNN has the potential to be further developed, validated and adopted for screening and management of keratoconus.

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ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

Cited by 205 publications

References 59 publications

Superficial Macula Capillary Complexity Changes Are Associated With Disability in Neuromyelitis Optica Spectrum Disorders

Superficial Macula Capillary Complexity Changes Are Associated With Disability in Neuromyelitis Optica Spectrum Disorders

Quantitative Assessment of Experimental Ocular Inflammatory Disease

Keratoconus detection of changes using deep learning of colour-coded maps

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